Method of and system for projecting digital information on a real object in a real environment

ABSTRACT

A method of projecting digital information on a real object in a real environment includes the steps of projecting digital information on a real object or part of a real object with a visible light projector, capturing at least one image of the real object with the projected digital information using a camera, providing a depth sensor registered with the camera, the depth sensor capturing depth data of the real object or part of the real object, and calculating a spatial transformation between the visible light projector and the real object based on the at least one image and the depth data. The invention is also concerned with a corresponding system.

This application is entitled to the benefit of, and incorporates byreference essential subject matter disclosed in PCT Application No.PCT/EP2012/077060 filed on Dec. 28, 2012.

BACKGROUND OF THE INVENTION

1. Technical Field

The invention is related to a method of and system for projectingdigital information on a real object in a real environment. Further, theinvention is related to a computer program product comprising softwarecode sections for performing the method when running on a computersystem.

2. Background Information

Augmented reality (AR) systems could enhance a real environment bydirectly visually augmenting the real environment by computer-generateddigital information. For example, such digital information is virtualinformation for augmenting visual impressions of the real environment.Typical applications are known as, for example, so-calledprojector-based AR, projective AR or spatial AR, such as referred to inreference [1]. The digital information can be any type of visuallyperceivable data such as objects, texts, drawings, videos, or theircombination. The direct augmentation of the real environment could berealized by projecting the computer-generated digital information onto asurface of a real object of the real environment or a part of the realenvironment using a projector.

Projective AR has many applications, such as prototyping forarchitecture design, e.g. described in reference [2], and carmanufacture, e.g. described in reference [3], ubiquitous computing fornovel computer user interface development, e.g. described in reference[4], information displaying, e.g. described in reference [5], or shoedesign, e.g. described in reference [6].

In order to have a desired alignment between projected visual digitalinformation and real objects that will be augmented in the realenvironment, spatial transformations between the real objects and aprojector that projects the digital information have to be known. Forthis, a calibration procedure is often performed to estimate the spatialtransformation based on 2D-2D, 2D-3D, or 3D-3D correspondences which isa challenging step for building up projective AR systems. A camera isgenerally required for such calibration procedure.

Many calibration methods have been proposed and developed to compute aspatial transformation between a projector and a real object. Forexample, Jundt et al. in reference [3] describe a method to displayvisual data about a car on a car's surface using a camera and aprojector. The camera and the projector should be rigidly coupled inorder to calibrate them once and then assume a projector-car extrinsic(i.e. relative spatial position and orientation) computed fromprocessing visual data acquired by the camera. The camera detects visualmarkers attached to the car in order to estimate a spatialtransformation between the camera and the car. In this way, a spatialtransformation between the projector and the car could be determined. Aproblem of the approach is that the position and orientation of themarkers relative to the car's coordinate system has to be measuredbeforehand.

Extend3D, described in reference [6], is a commercial projective ARsystem which tracks a set of markers based on camera sensors that arerigidly attached to a projector. These two developed systems haveseveral limitations. The calibration cannot be checked by an independententity. An additional calibration procedure between the cameras and theprojectors has to be performed frequently. This procedure cannot beconducted on arbitrary objects, but needs to assume a flat surface, orsimilar. Furthermore, the visual markers themselves need to beregistered with the car, beforehand, which introduces additional errorsources.

Raskar et al. in reference [2] developed a projective AR system forsupporting architectural design. They propose a calibration method formultiple projectors to ensure that projected images are geometricallyaligned. Their system requires manually adjusting projected imagetexture coordinates to visually align with the physical model (realobject).

Kurz et al. in reference [6] present a calibration method to build aspatial transformation between a projector and an indoor physical scene(i.e. real object) based on using a laser-pointer rigidly coupled with acamera. The camera and laser system is mounted in a robotic pan-tiltapparatus such that the movement of the camera and laser could becontrolled for scanning the indoor environment and calibrating theprojector with the indoor environment. However, the robotic pan-tiltapparatus is expensive and the controlled movement of the camera andlaser system is difficult to realize. This is mainly because that thesystem requires a very precise hand-eye calibration of thepan-tilt-camera that provides the camera position and orientation withrespect to the pan-tilt unit. Also sampling the environment with thelaser takes a lot of time, as only one point at a time can be sensed.

Fuchs et al. in reference [7] describe the use of structured light fromprojectors for gathering depth information to be used later invisualization. They do not project virtual information on real objects,but rather use a head mounted display.

Lim in reference [8] employs multiple projectors and one camera forscene reconstruction. They calibrate spatial relationships betweenmultiple projectors based on known transformations between the cameraand each of the projectors. During the calibration, they require aprojection on a board, which means that they cannot calibrate byprojecting on any object of interest. They would need anextra-calibration to be able to estimate the calibration data andtransformations in the coordinate system of the object of interest. Thegoal of reference [8] is to reconstruct the environment. It does notpropose any calibration method to compute a spatial transformationbetween a projector and a real object.

In order to calibrate the spatial relationship between a projector and areal object and estimate the intrinsic parameter of the projector inprojective AR applications or systems, all of the previous methodsrequire a complex procedure and/or an expensive hardware setup. Thisdefinitely reduces the usability and efficiency of the projective ARapplication or systems.

Therefore, it would be beneficial to develop a method and system thatenable a projective AR system to accurately project digital informationon top of real objects without an expensive hardware setup and withoutthe need of conducting lengthy calibration procedures.

SUMMARY OF THE INVENTION

According to an aspect, there is provided a method of projecting digitalinformation on a real object in a real environment, comprisingprojecting digital information on a real object or part of a real objectwith a visible light projector, capturing at least one image of the realobject with the projected digital information using a camera, providinga depth sensor registered with the camera, the depth sensor capturingdepth data of the real object or part of the real object, andcalculating a spatial transformation between the visible light projectorand the real object based on the at least one image and the depth data.

According to another aspect, there is provided a system for projectingdigital information on a real object in a real environment, comprising avisible light projector adapted for projecting digital information on areal object or part of a real object in a real environment, a cameraadapted for capturing at least one image of the real object with theprojected digital information, a depth sensor registered with the cameraand adapted for capturing depth data of the real object or part of thereal object, and a processing unit arranged for calculating a spatialtransformation between the visible light projector and the real objectbased on the at least one image and the depth data.

According to a particular implementation, in a preferred embodiment themethod comprises estimating a spatial transformation between a RGB-Dcamera system and the real object based on a known 3D model of the realobject and computing intrinsic parameters of the projector and a spatialtransformation between the projector and the real object based onprojecting one or more visual patterns on the surface or surfaces of thereal object or a part of the real object using the projector, andcapturing a depth map of the projected visual patterns using the RGB-Dcamera system.

According to an embodiment, the method further comprises estimating adepth of the digital information using the depth data.

Particularly, the method may further comprise estimating a 3D positionof the digital information using the depth data and the at least oneimage.

In a preferred implementation, the depth sensor and the camera arecombined to form a subsystem in which the depth sensor and the cameraare interrelated (i.e. have a known transformation between them), themethod further including the step of calculating a spatialtransformation between the subsystem of depth sensor and camera and thereal object.

For example, calculating a spatial transformation between the subsystemof depth sensor and camera and the real object is based on a 3D geometrymodel of the real object or a part of the real object and a 3Ddescription of the real object or a part of the real object from one ormore images and depth data of the real object captured by the subsystemof depth sensor and camera.

Particularly, the method may further comprise estimating a depth of thedigital information using a 3D geometry model of the real object or apart of the real object and the calculated spatial transformationbetween the subsystem of depth sensor and camera and the real object togain second depth data.

According to an embodiment, the method further includes the steps ofprojecting as the digital information at least one visual pattern onto asurface of the real object using the visible light projector, andcapturing depth data of the projected visual pattern using the depthsensor and camera.

According to an embodiment, the method further comprises the steps ofcalculating a spatial transformation between the visible light projectorand the subsystem of depth sensor and camera, calculating or providingintrinsic parameters of the visible light projector, and computing thespatial transformation between the visible light projector and the realobject based on the spatial transformation between the visible lightprojector and the subsystem of depth sensor and camera, the spatialtransformation between the subsystem of depth sensor and camera and thereal object, and preferably the intrinsic parameters of the visiblelight projector.

According to another embodiment, the method further comprises the stepsof transforming the depth data of the projected visual pattern from acoordinate system of the subsystem of depth sensor and camera to anobject coordinate system of the real object based on the spatialtransformation between the subsystem of depth sensor and camera and thereal object, calculating or providing intrinsic parameters of thevisible light projector, and computing the spatial transformationbetween the visible light projector and the real object based on thetransformed depth data, and preferably the intrinsic parameters of thevisible light projector.

For example, the subsystem of depth sensor and camera is a RGB-D camerasystem with the camera being a RGB (RGB=Red/Green/Blue) camera (with Dstanding for depth sensor).

Particularly, the depth sensor is capturing depth data of the realobject or part of the real object without relying on the visible lightprojector.

According to an embodiment, third depth data is created using a spatialtransformation between the subsystem of depth sensor and camera and thevisual light projector and intrinsic parameters of the visual lightprojector, and projecting an item of digital information on the realobject, which is extracted from the image of the camera.

For example, the depth data captured by the depth sensor are calledherein in the following as first depth data, and the method furthercomprises the step of computing a difference between any combination ofthe first depth data, the second depth data and the third depth data.

According to an embodiment, if a certain distance of the visible lightprojector to the real object is determined to be reached or exceeded,informing the user about the need to calibrate, or automaticallystarting a calibration procedure.

According to another embodiment, a distance of the visible lightprojector to the real object is displayed as a visual information on thereal object using the visual light projector.

According to an embodiment, the method comprises the step of trackingthe real object using the subsystem of depth sensor and camera.

For example, one or more visual markers are added into the realenvironment to support the tracking.

In a preferred embodiment, an Iterative Closest Point (known as ICP)algorithm, as referred to in more detail below, is used to initialize apose of the depth sensor.

According to an embodiment, pose data of the visible light projector areused to set specific parameters of the projector, such as focus or zoomor brightness.

Particularly, a brightness of the visible light projector may getsmaller if the projector gets closer to the real object.

Particularly, the zoom of the visible light projector increases theprojector's field of view when getting closer and decreases the field ofview when moving further away.

According to a further embodiment, the method further comprises the stepof interaction by the user on top of the surface of the real objectusing the subsystem of depth sensor and camera in order to recognizetouches by the user.

According to another aspect, the invention is also related to a computerprogram product adapted to be loaded into the internal memory of adigital computer system, comprising software code sections by means ofwhich the steps and features as described above are performed when saidproduct is running on said computer system. Further, the invention canalso be partly implemented in hardwired logic and may be related to aprogrammed logical circuit that is arranged for performing a method asdescribed herein.

According to an embodiment of the system according to the invention, thecamera and the depth sensor are integrated into a common housing.

Preferably, the camera and the depth sensor are functional units of aRGB-D camera.

In a particular implementation, the visible light projector, the cameraand the depth sensor are part of a hand-held or head-mounted device.

In a preferred implementation, the camera includes a visible lightcamera, preferably an RGB camera, and the depth sensor includes aninfrared light projector and an infrared light camera.

According to an embodiment, the visible light projector, the camera andthe depth sensor are integrated into a common housing, wherein thevisible light projector is separated from the camera and the depthsensor by insulating or heat-damping material.

According to another embodiment, the visible light projector, the cameraand the depth sensor are integrated into a common housing, wherein abase plate of the housing is made of a carbon fiber laminate.

According to a further embodiment, the system includes calibrationmeans, which inform the user about a need for a new calibration of thesystem in case a temperature difference between a temperature of arecent calibration and a current temperature exceeds a threshold, orwhich automatically conducts a self-calibration of the system.

For example, the visible light projector contains at least one of thefollowing elements: a vent, zoom optics, variable focus lenses.

According to an embodiment, the system further includes an infraredlight projector, preferably used as part of the depth sensor, whereinthe infrared light projector does not contain at least one of thefollowing elements: a vent, zoom optics, variable focus lenses.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the invention and embodiments thereof will now be describedwith reference to the drawings, in which

FIG. 1A shows an exemplary system setup according to an aspect of theinvention, wherein in a RGB-D camera system and a visible lightprojector are not rigidly coupled with each other,

FIG. 1B shows the system setup according to FIG. 1A with an additionalvisualization of coordinate systems used according to aspects of theinvention,

FIG. 2 shows another exemplary system setup according to an aspect ofthe invention where a RGB-D camera system and a visible light projectorare rigidly coupled with each other,

FIG. 3 shows a flowchart diagram of a method according to an embodimentof the invention,

FIG. 4 shows a flowchart diagram of a method according to anotherembodiment of the invention,

FIG. 5 shows an exemplary hand-held device including a visible lightprojector and a RGB-D camera system which may be used according toaspects of the invention,

FIG. 6 shows an exemplary head-mounted device including a visible lightprojector and a RGB-D camera system which may be used according toaspects of the invention,

FIG. 7 shows an advantageous hardware-setup according to aspects of theinvention,

FIG. 8 shows a flowchart diagram of an exemplary ICP algorithm which maybe used in connection with a method according to aspects of theinvention.

DETAILED DESCRIPTION OF THE INVENTION

In the following description of embodiments and aspects of theinvention, it is mainly referred to the system setup according to FIGS.1A and 1B, which are only meant as exemplary system setups forexplaining the invention without limiting the invention to anyparticular aspects shown therein. Generally, a system setup according tothe invention is capable of projecting digital information on a realobject in a real environment. In the present example, the real object isa car 11, however any other real object or parts thereof may also beused. The system includes a visible light projector 15 adapted forprojecting digital information on a real object, such as the car 11, orpart of a real object in a real environment, a camera 12 (particularly avisible light camera, such as a RGB camera or other types as set outherein below) adapted for capturing at least one image of the realobject 11 with projected digital information, and a depth sensor(comprising, in the present example, an infrared light camera 13 and aninfrared light projector 14) which is registered with the camera 12 andadapted for capturing depth data of the real object 11 or part of thereal object 11. Using an infrared projector and infrared camera as depthsensor is a technique for measuring depth data of a real object, onwhich the infrared light is projected and captured by the infraredcamera, which is known to the skilled person. Other depth sensingtechniques may also be used.

The visible light camera 12 and the depth sensor 13, 14 form a subsystem16 of camera 12 and depth sensor 13, 14 in which camera and depth sensorare interrelated, i.e. are interrelated by a known spatialtransformation between them. For example, the subsystem 16 is an RGB-Dcamera system, as described in more detail below. The system furtherincludes a processing unit arranged, inter alia, for calculating aspatial transformation between the visible light projector 15 and thereal object 11 based on the at least one image captured by the camera 12and the depth data measured by the depth sensor 13, 14, and forcalculating any other transformations needed therefor and calculationsas set out herein in more detail below. The processing unit 17 may beany suitable processing unit as typically used in compact or distributedcomputer systems for such applications, such as a CPU of a personalcomputer or any other suitable processor or processing unit or logic.

According to a preferred embodiment of the present invention,computer-generated digital (also called herein virtual) information isprojected on surfaces of a real object, in the present case of realobject 11, in a real environment using visible light projector 15 byestimating a spatial transformation between RGB-D camera system 16 andthe real object 11 based on a known 3D model of the real object andcomputing the intrinsic parameters of the projector 15, and a spatialtransformation between the projector 15 and the real object 11 based ondigital information (preferably one or more visual patterns) projectedfrom the projector 15 onto the real object 11 or part of the real object11 and a depth map of the projected digital information (e.g., visualpatterns) captured by the RGB-D camera system 16.

According to aspects of the invention, there is proposed a method ofprojecting digital information on a real object in a real environmentand calculating, particularly in a calibration procedure, a spatialtransformation between a visible light projector projecting the digitalinformation on the real object or part of the real object and the realobject or part of the real object. A spatial relationship or spatialtransformation specifies how an object is located in 3D space inrelation to another object in terms of translation and rotation. AnRGB-D camera system is a capturing device that is capable of capturingan RGB-D image of a real environment or a part of a real environment. AnRGB-D image is an RGB image with a corresponding depth map (i.e. depthdata related to real objects captured in the image). Instead of adepth-image, the system could also provide a 3D model relative to thecamera coordinate system. The proposed invention can be easilygeneralized to any camera providing an image format (color or grayscale) that additionally provides depth data. It is not restricted tocapture systems providing color images in the RGB format. It can also beapplied to any other color format and also to monochrome images, forexample to cameras providing images in grayscale format. In anadvantageous implementation the visible-light camera could be a highdynamic range camera or a camera equipped with a filter restricting itsresponsiveness to certain light waves, e.g. making the camera only seeblue colors. The depth images do not need to be provided in the sameresolution as the visual (color/grayscale) camera image. The so-calledRGB-D system can be any combination of devices that are mounted andcalibrated together to provide a set of photometric information anddepth information of a set of physical points in the environment.

The real environment consists of one or more real objects. A real objectcould be any physically existent object in the world, such as a car, atree, a building, a human, or a stone.

A projector is an optical device that projects an image (i.e. visualdigital information) onto a physical surface of a real object or a partof a real object. Visual digital (or virtual) information can be anytype of visually perceivable data such as objects, particularly 3Dobjects, texts, drawings, videos, user-interface elements (e.g. buttons)or their combination.

A 3D model of a real object describes the 3D geometry of the realobject. Geometry describes one or more attributes including, but notlimited to, shape, symmetry, geometrical size, and structure.

A problem of calibrating a spatial relationship between a projector anda real object is to determine a transformation including translation andorientation between the projector and the real object.

In the following, given the exemplary background scenario as shown inFIGS. 1A and 1B, it is referred to FIG. 3 which shows an embodiment of amethod according to the invention as a flowchart.

A 3D description (for example in a form of a point cloud located on thesurface) of the real object 11 or a part of the real object 11 isconstructed based on a depth map of the real object or a part of thereal object from depth data captured by the RGB-D camera system 16 (Step31 in FIG. 3). The 3D description of the real object 11 is constructedin the RGB-D camera coordinate system 102 (FIG. 1B). A spatialtransformation 104 between the RGB-D camera system 16 and the realobject 11 can be estimated numerically by finding the best geometricmatch between the reconstructed point cloud expressed in the RGB-Dcamera coordinate system 102 and a point cloud of the known 3D model ofthe real object 11 (provided in step 32) expressed in the real objectcoordinate system 101. This can be achieved by using an iterativeclosest point (ICP) algorithm, which is per se known to the skilledperson, and as described in more detail below. Other methods could bebased on matching specific features of both models, based on topology,curvature or shape, as described in reference [16]. The point cloud ofthe known 3D model of the real object could be obtained by sampling the3D model via ray-casting, for example. It is also possible to sample the3D model by triangles and then employ the ICP algorithm based onpoint-to-plane distance to estimate the spatial transformation betweenthe RGB-D camera system and the real object (Step 33 in FIG. 3). To theperson skilled in the art it is clear, the known 3D model of the realobject could also be provided in form of a mathematical description,e.g. SPLINES or NURBS.

The projector 15 projects a visual pattern or patterns with knowngeometry onto the surface of the real object 11 or a part of the realobject 11 (Step 35 in FIG. 3). The visual pattern could be of variousforms, such as points, lines, multiple points, multiple lines, grids,circles, cross-hairs, thick stripes, binary-coded patterns, gray codepatterns, color-coded stripes, and random textures. The 3D position(s)of the projected visual pattern(s) in the RGB-D camera coordinate system102 could be directly obtained from a depth map captured by the RGB-Dcamera system (Step 36 in FIG. 3). In an alternative implementation, the3D positions of the projected visual pattern(s) can be retrieved fromthe 3D model of the real object, after the spatial transformation 104between the RGB-D camera system and the real object has been estimated.

A spatial transformation 106 between the projector 15 and the RGB-Dcamera system 16 as well as intrinsic parameters of the projector 15could be computed based on the 2D coordinates of the visual pattern(s)in the projector coordinate system 103 and corresponding 3D coordinatesof the visual pattern(s) in the RGB-D camera coordinate system 102 (Step37 in FIG. 3). In case the intrinsic parameters of the projector areknown, they could be used instead of being re-estimated. The intrinsicparameters are typically used to calculate the spatial transformationbetween visible light projector and the subsystem of depth sensor andcamera. Once spatial transformations have been determined, the intrinsicparameters may only be used in the step of visualization of informationon the real object.

It should be noted that the described procedures of computing thespatial transformation 104 between the real object and the RGB-D camerasystem and computing the spatial transformation 106 between theprojector and the RGB-D camera system could be performed in parallel incase the 3D positions of the projected visual patterns are obtained fromthe depth map.

Finally, the spatial transformation 105 between the projector 15 and thereal object 11 is computed based on the estimated spatial transformation104 between the real object 11 and the RGB-D camera system 16 and theestimated spatial transformation 106 between the projector 15 and theRGB-D camera system 16 (Step 38 in FIG. 3).

Furthermore, the present invention does not require the projector 15 andthe RGB-D camera system 16 to be rigidly coupled or to have a pre-knownspatial transformation between the projector 15 and the RGB-D camerasystem 16 (see FIG. 1A). This increases the usability and flexibility ofthe present invention compared to the prior art, as described inreferences [3, 6]. Especially because RGB-D systems are available ascompact systems without movable parts or optics, which are easy tocalibrate and their physical structure will not change easily, andtherefore calibration will normally not change. On the other side,projectors are often equipped with movable optics and show large changesin temperature. They also often have large housings, damped optics andvents. All these characteristics make them strong at displaying verybright and high-quality images, but make them very hard to calibratesustainably.

Another embodiment of a method according to the invention is illustratedin FIG. 4. Steps 41-46 are corresponding to steps 31-36 as describedabove with reference to the embodiment of FIG. 3. As described above,the registration between the RGB-D camera system 16 and the real object11 is determined, and projecting visible pattern(s) onto the real object11 leads to 2D-3D correspondences between the projector coordinatesystem 103 and the RGB-D camera coordinate system 102.

In step 47, the 3D coordinates of these correspondences are transformedfrom the RGB-D camera coordinate system 102 to the object coordinatesystem 101 using the transformation determined using the ICP algorithm.Finally, the spatial transformation between the projector 15 and thereal object 11 (and optionally the intrinsic parameters of theprojector) is computed based on 2D-3D correspondences between theprojector 15 and the object coordinate system 101 in step 48.

Possible Implementations of an RGB-D Camera System:

The RGB-D camera system could be a time of flight (TOF) camera system.Kolb et al. in reference [9] give an overview on state of the art ontime-of-flight camera sensors and applications. An RGB-D camera system16 could also be built using an RGB camera 12, an infrared light camera13 and an infrared projector 14 (see FIG. 1A). The RGB camera 12, theinfrared light camera 13 and the infrared light projector 14 aretypically rigidly coupled and their spatial relationships are known. Bythis, the RGB camera, the infrared light camera and the infrared lightprojector could be defined in a common coordinate system named as theRGB-D camera coordinate system 102. Advantageously, the three sensorsare all tightly attached to one common part, e.g. a solid block of metalor a carbon fiber laminate part.

Several methods, such as described in references [2,6], have beendeveloped to calibrate a spatial relationship between a camera and aprojector. A common way is to let the projector project a pattern withknown geometry onto a physical surface and the camera capture theprojected pattern. This could build correspondences between theprojector coordinate system and the camera coordinate system, and thusthe transformation between the projector and the camera could beestimated.

An infrared light projector and an infrared light camera together couldproduce a depth map of a real object or a part of the real object. Forthis, the infrared projector projects a pattern with known geometry ontothe real object, and the infrared camera captures an image of theprojected pattern. From the image of the projected pattern, a depth mapof the real object could be generated. As the RGB camera, the infraredcamera and the infrared projector were calibrated in the common RGB-Dcamera coordinate system, a RGB-D image could be obtained from a depthmap and a RGB image of the RGB camera.

There are some commercially available RGB-D camera systems based onusing an RGB camera, an infrared camera and an infrared projector, suchas the known Kinect system from Microsoft or Xtion Pro from Asus. Thesesystems are examples of off-the-shelf commodity cheap consumer devices.U.S. Pat. No. 8,150,142 B2 and U.S. Pat. No. 7,433,024 B2 describedetailed ways of a possible implementation of an RGB-D sensor.

An advantageous version of a depth sensor in this invention is capableof delivering a depth image at interactive frame rates (e.g. higher than5 frames per second).

Possible ICP Algorithm Implementation:

Iterative Closest Point (ICP) (described in, e.g., reference [12]) is analgorithm to spatially register two partially overlapping 3D models,which are often represented by 3D point clouds. The aim of the algorithmis to find a 6 DoF (DoF: Degrees of Freedom) rigid body transformation(comprising a 3D rotation and a 3D translation) that transforms onemodel to be registered with the other, see e.g. FIG. 8 which isdescribed in the following:

Given a reference model R (step 81), a current model C (step 82), and aninitial guess of the transformation between C and R (step 83), themethod initially transforms C (step 84) with the provided initial guess.Note that in the simplest case, the initial guess can be an identitytransform. The iterative method now selects point samples R′ and C′ fromthe models R and C (step 85) and then establishes matches between thesetwo sets of points (step 86). In the simplest case, a matchingcorrespondence for a point in C′ is determined as the closest point inR′. In a subsequent step (step 87) weights are computed for every match.Note that the simplest implementation of this is to assign equal weightsto all matches. After assigning an error metric (step 88), e.g. theroot-mean-square of the distance between the matching points, atransformation is found that minimizes this error metric (step 89).

An exit criteria (step 810) decides, if the found solution should berefined in an additional iteration or not. If so, C is transformed withthe found transformation (step 811) before the next iteration starts byselecting point samples R′ and C′ from the models R and C (step 85).Otherwise, the accumulated transformation, i.e. the accumulation of alltransformations that were applied to C during the iterative approach andthe last transformation found in step 89 is returned as finaltransformation aligning C with R (step 812).

Potential Additional Uses of the Depth Sensor:

Having a depth-sensor capable of generating depth images has additionaladvantages. One advantage can be the implementation of a differencemeasurement in order to find discrepancies between the virtual model(e.g. a computer aided design (CAD) model) of the real object and theactual geometry of the real object. Because the ICP algorithm is capableof handling partial errors or differences between two 3D models, thealgorithm will in most cases be able to align a partially differentvirtual model to depth data coming from the depth sensor. The differencecan then be calculated, e.g. between a vertex or point of one model tothe closest vertex or point of the other model. Assigning differentdistances different colors the projector could color parts of the realobject according to their difference to the virtual 3D model.

Another advantage can be the additional implementation of atouch-interaction with the real object. From the state of the art (e.g.reference [11]), techniques are known to retrieve body gestures fromdepth data. Body tracking could be used to retrieve a roughhead-position of a user of the system. This head position can be used torefine the displayed virtual data to look more realistic and be betteraligned to the real object, than just assuming the user to have thevisible projector's viewpoint. More on this rendering technique can befound in reference [13].

Body tracking could also be used to retrieve a user's hand position orto retrieve the nearest body part of the user that is close to the realobject. The system could interpret any body part or only a hand, whichis assumed to be closer than a certain threshold (e.g. 10 cm) from thereal object, to be a mouse-click or a mouse-event, where the consideredmouse-position corresponds to the nearest 3D point on the 3D model,projected into 2D coordinates according to the projector intrinsicparameters. Using this information and combining it, or by directlyidentifying the subpart of a 3D model with known techniques, all kindsof user-interactions could be triggered. For example a virtual post-itcould be attached to the 3D model at this position or a part could beanimated or highlighted. Even design-tasks could be conducted, bydragging virtual objects on top of the real object or “virtuallypainting” on top of the real object.

An advantageous addition to the touch-interaction aspect is to detectthe user's touching the real object by using capacitive sensing or bymeasuring a current when the user touches the real object. A capacitivesensing circuit could be connected to the real object and provide aninput signal to the processing unit when it detects a change in capacityof the real world object. Insulating the real object from the ground andconnecting the real object to a voltage (e.g., by a high resistance) andmeasuring the current that incurs when a user touches the object,connecting it to the ground, could be an alternative implementation. Thedepth sensor data can then be evaluated in order to assume the positionof where the real object might have been touched.

Wikipedia, retrieved Dec. 17 2012, provides the following possible waysof implementing a capacitive sensing circuit(http://en.wikipedia.org/wiki/Capacitive_sensing):

“Capacitance is typically measured indirectly, by using it to controlthe frequency of an oscillator, or to vary the level of coupling (orattenuation) of an AC signal. The design of a simple capacitance meteris often based on a relaxation oscillator. The capacitance to be sensedforms a portion of the oscillator's RC circuit or LC circuit. Basicallythe technique works by charging the unknown capacitance with a knowncurrent. (The equation of state for a capacitor is i=C dv/dt. This meansthat the capacitance equals the current divided by the rate of change ofvoltage across the capacitor.) The capacitance can be calculated bymeasuring the charging time required to reach the threshold voltage (ofthe relaxation oscillator), or equivalently, by measuring theoscillator's frequency. Both of these are proportional to the RC (or LC)time constant of the oscillator circuit. The primary source of error incapacitance measurements is stray capacitance, which if not guardedagainst, may fluctuate between roughly 10 pF and 10 nF. The straycapacitance can be held relatively constant by shielding the (highimpedance) capacitance signal and then connecting the shield to (a lowimpedance) ground reference. Also, to minimize the unwanted effects ofstray capacitance, it is good practice to locate the sensing electronicsas near the sensor electrodes as possible. Another measurement techniqueis to apply a fixed-frequency AC-voltage signal across a capacitivedivider. This consists of two capacitors in series, one of a known valueand the other of an unknown value. An output signal is then taken fromacross one of the capacitors. The value of the unknown capacitor can befound from the ratio of capacitances, which equals the ratio of theoutput/input signal amplitudes, as could be measured by an ACvoltmeter.”

The user's touch could also be used without measuring the user'sposition and could simply trigger a next step in a possible contentworkflow. For example the system first displays a virtual engine on areal car and after touching the car the system switches to displayingthe structure of the gear-box.

In order to extend the range of the depth sensor or addressing more ofthe user of the system, a second depth sensor could be mounted in afixed spatial relationship to the first depth sensor. The two depthsensors could be triggered sequentially or only on demand.

Compared to the methods as described in references [3,6], the presentinvention does not require a rigid coupling between the visible lightprojector and the RGB-D camera system, and does not need a visual markeror texture foreground rigidly attached to the real object. Therefore,the present method simplifies the calibration procedure of computing thespatial transformation between the real object and the visible lightprojector and estimating the intrinsic parameters of the projector.Furthermore, a marker-object calibration between the visual marker andthe real object is not required in the present method compared to themethods as described in [3,6], which removes the errors caused by themarker-object calibration.

Compared to the method as described in reference [6], that requiresrobotic device to move a pointer attached with a camera for scanning thereal object, the present invention employs a different idea, preferablybased on a RGB-D camera system. The present invention could reduce thecalibration time compared to the method as described in [6]. Moreover,as the two described procedures of computing the spatial transformationbetween the real object and the RGB-D camera system and computing thespatial transformation between the projector and the RGB-D camera systemcould be performed in parallel, the present invention could furtherspeed up the calibration. Advantageously, the RGB-D camera system iscapable of providing depth images of a resolution higher than 50 times50 pixels at frame rates of above 10 Hz.

A further depth map of the real object or a part of the real objectcould be produced by projecting and capturing using the visible lightprojector and the RGB image of the RGB-D camera system. This depth mapshould be the same as the depth map provided from the RGB-D camerasystem. Assuming that the RGB-D camera system is fixed with respect tothe real object, whenever the projector moves away from the real objector away from the RGB-D camera system after the calibration of thespatial relationship between the projector and the real object, the twodepth maps will be different and the system should be re-calibrated.

In the following, it is referred to another embodiment of a systemaccording to FIG. 2. Similar as the system according to FIG. 1, there isprovided a RGB-D camera system 26 with an RGB camera 22, a depth sensorformed by infrared light camera 23 and infrared light projector 24, anda visible light projector 25 which projects digital information on areal object 21. Assuming that the relative transformation between theRGB-D camera system 26 and the projector 25 is fixed (see FIG. 2), it ispossible to detect a movement of the real object 21 after thecalibration of the spatial relationship between the projector 25 and thereal object 21. The spatial relationship between the projector 25 andthe real object 21 should be re-calibrated as soon as such movement isdetected.

The detection could be realized as follows. Let the visible lightprojector project visual patterns onto the top of the real object andlet the RGB-D camera system capture an image of the projected patterns,and then check whether the image positions of the projected patterns areat the desired position or not. The desired image positions can beestimated by computing intersections between the rays of the visualpatterns emitted from the projector and the 3D model of the real objectbased on the calibrated spatial relationship between the real object andthe projector. The 3D positions of the intersections could be expressedin the real object coordinate system. The desired image positions arethe re-projections of the intersections into the image coordinate systemof the RGB-D camera system based on the calibrated spatial relationshipbetween the real object and the RGB-D camera system.

The detection could also be realized based on depth maps obtained fromthe RGB-D camera system. If a current obtained depth map is differentfrom a depth map captured at the calibration from the RGB-D camerasystem, there may exist a movement of the real object after thecalibration.

Assuming that the relative transformation between the RGB-D camerasystem 26 and the projector 25 is fixed (see FIG. 2), after thecalibration of the spatial relationship between the projector and thereal object, the RGB-D camera system could track the movements of thereal object using computer vision methods or the ICP, as used duringinitialization, and the spatial relationship between the projector andthe real object could be updated accordingly. Furthermore, the systemparameters of the visible light projector could be adjusted according tothe pose of the real object with respect to the RGB-D camera system fromthe tracking. For example, the brightness of the projector gets lowerwhen it is closer to the real object.

Adding visual markers to the real object after the calibration couldenable a robust tracking of the real object using the RGB-D camerasystem.

The present calibrated system of the RGB-D camera system and theprojector could further support to detect user's interaction on top ofthe real object by using the depth map from the RGB-D camera system inorder to see touches.

An advantage of the invention is that, after calibration, twoindependent sources of depth data exist. Therefore, it is possible toevaluate the quality of the projective AR system and its calibration bycomparing two sets of depth data regarding the real object, one from theRGB-D camera system and one from using the visible light projector andRGB images of the RGB camera. The depth data provided from using theprojector and RGB images of the RGB camera could be realized by lettingthe projector project visual patterns with known geometry onto physicalsurfaces of the real object and the RGB camera capture the RGB images ofprojected patterns. Having intrinsic and extrinsic data available, thetwo models can be registered and a quality measurement, e.g. the averagedistance of reconstructed points from the projector to the RGB-D modelcan be returned.

Another possible way of checking the quality of the calibration is toproject detectable visible light information onto the real object.According to the intrinsic parameters of the visible light camera, theintrinsic parameters of the visible light projector and the spatialrelationship of visible light projector to the real object and to thevisible light camera, the detectable visible light can be expected tohave a certain position in the visible light camera image. The intrinsicparameters of the visible light camera are known from a pre-calibrationprocedure. The distance between the detectable visible lightinformation's expected position and its real position can be returned inpixels as a quality measure. Alternatively, the difference can bemathematically projected onto the real object and converted tomillimeters.

According to an aspect of the invention, the visible light camera andthe depth sensor are integrated into a common housing, particularly arefunctional units of a RGB-D camera system as described. The system,including the visible light projector, the depth sensor and the visiblelight camera could also be implemented in a miniaturized way to form ahand-held or head-mounted device.

A potential implementation is respectively shown in FIGS. 5, 6 and 7.The system of a visible light projector and a RGB-D camera systemcould—if miniaturized—be formed as a hand-held or head-mounted device.An exemplary hand-held device 50 comprising the projector and the RGB-Dcamera system shown in FIG. 5 may include a handle 51, a visible lightprojector 55 and a RGB-D camera system. The RGB-D camera system mayfurther include an infrared projector 52, an infrared camera 53, and aRGB camera 54.

An exemplary head-mounted device 60 comprising a visible light projectorand a RGB-D camera system shown in FIG. 6 may include a fixationcomponent 61, a visible light projector 65 and a RGB-D camera system.The RGB-D camera system may further include an infrared projector 62, aninfrared camera 63, and a RGB camera 64. The fixation component 61 couldsupport a rigid fixation between the head-mounted device and the head ofa user who perceives with his eyes 66 the projected digital information.

Another advantageous hardware-setup of a system according to aspects ofthe invention is shown in FIG. 7. A visual light projector, which istypically hard to calibrate, and a RGB-D camera system are combined in acommon housing 70, but are at the same time functionally separated. Theelements of the RGB-D camera system, which in this embodiment comprisesan infrared light projector 74, a visible light camera 75 and aninfrared light camera 76, are attached to a solid construction element,therefore their spatial relationship should be robust against movementor even impacts during transport. At least one of the visible lightcamera 75, the infrared light projector 74 and the infrared light camera76 can, in one possible implementation, be equipped with fixed focusoptics. The hard-to-calibrate visible light projector comprises complexoptics 71, which may include dampers, zoom and focus mechanics. It has ahigh-energy light source 79 and a spatial light modulator 78. Because ofthe high-energy light source, the visible light projector has largechanges in temperature after it is turned on (indicated by the optionaltemperature indicator or sensor 72). Large changes in temperature are amajor source of decalibration, because the change in size ofconstruction element may change the spatial relationship of systemcomponents.

Therefore, the visible light projector and the RGB-D camera system arein this embodiment separated by insulating or heat-damping material 73.Advantageously, the base plate 70-1 of the housing is made of carbonfiber laminate and the RGB-D camera system and the visiblelight-projector are tightly attached to the base-plate 70-1 and notattached to the side wall 70-2 of the housing. The housing side wall70-2 could be attached so that it can slightly move against the baseplate 70-1. Advantageously, the housing can have at least one fresh-airsupply or hot-air outlet 70-3. Advantageously, the system could have atleast one temperature sensor 72. The system could inform the user aboutthe need for a new calibration in case the temperature differencebetween the last calibration and the current temperature exceeds a giventhreshold. Alternatively, the system could automatically conduct aself-calibration.

While the invention has been described with reference to exemplaryembodiments and applications scenarios, it will be understood by thoseskilled in the art that various changes may be made and equivalents maybe substituted for elements thereof without departing from the scope ofthe claims. Therefore, it is intended that the invention not be limitedto the particular embodiments disclosed, but that the invention willinclude all embodiments falling within the scope of the appended claimsand can be applied to various application in the industrial as well ascommercial field.

REFERENCES

-   10. Sanni Siltanen, Theory and applications of marker-based    augmented reality. Espoo 2012. VTT Science 3.    http://www.vtt.fi/inf/pdf/science/2012/S3.pdf-   11. Raskar, Ramesh, Greg Welch, and Wei-Chao Chen. “Table-top    spatially-augmented realty: bringing physical models to life with    projected imagery.” Augmented Reality, 1999. (IWAR'99) Proceedings.    2nd IEEE and ACM International Workshop on. IEEE, 1999.-   12. DE 10 2010 013 420 A1-   13. Rekimoto, Jun, and Masanori Saitoh. “Augmented surfaces: a    spatially continuous work space for hybrid computing environments.”    Proceedings of the SIGCHI conference on Human factors in computing    systems: the CHI is the limit. ACM, 1999.-   14. Kurz, D., Häntsch, F., Grosse, M., Schiewe, A., and Bimber, O.,    Laser-Pointer Tracking in Projector-Augmented Architectural    Environments, In Proc. IEEE and ACM International Symposium on Mixed    and Augmented Reality (ISMAR2007), pp. 19-26, Nara, Japan, 2007-   15. Extend3D (from Website 9 Dec. 2012    http://www.extend3d.de/en/solutions/design/)-   16. Fuchs, Henry, et al. “Augmented reality visualization for    laparoscopic surgery.” Medical Image Computing and Computer-Assisted    Interventation—MICCAI'98 (1998): 934-943.-   17. U.S. Pat. No. 8,172,407 B2-   18. Andreas Kolb, Erhardt Barth, Reinhard Koch, Rasmus Larsen:    Time-of-Flight Sensors in Computer Graphics. Eurographics 2009.-   10. O. Bimber and R. Raskar. Spatial Augmented Reality: Merging real    and virtual worlds. A K Peters LTD, 2005.-   11. J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R.    Moore, A. Kipman, A. Blake: Real-Time Human Pose Recognition in    Parts from Single Depth Images. Retrieved from    http://research.microsoft.com/pubs/145347/BodyPartRecognition.pdf 17    Dec. 2012.-   17. Szymon Rusinkiewicz, Marc Levoy, Efficient Variants of the ICP    Algorithm. Third International Conference on 3D Digital Imaging and    Modeling-   18. BIMBER, O., WETZSTEIN, G., EMMERLING, A., AND NITSCHKE, C.    Enabling view-dependent stereoscopic projection in real    environments. In Proc. IEEE and ACM Int. Symposium on Mixed and    Augmented Reality. 2005-   19. U.S. Pat. No. 8,150,142 B2-   20. U.S. Pat. No. 7,433,024 B2-   21. M. Körtgen, G. Park, M. Novotni, R. Klein: 3D Shape Matching    with 3D Shape Contexts. Retrieved from    http://cg.tuwien.ac.at/hosting/cescg/CESCG-2003/MKoertgen/paper.pdf,    27 Dec. 2012

What is claimed is:
 1. A method of projecting digital information on areal object in a real environment, comprising: projecting digitalinformation on at least part of a real object with a visible lightprojector; capturing at least one image of the real object with theprojected digital information using a camera; capturing, by a depthsensor registered with the camera, depth data of the at least part ofthe real object, wherein the depth sensor and the camera comprise asusbsystem in which the depth sensor and the camera are interrelated;calculating a spatial transformation between the subsystem and the realobject based on the projected digital information in the at least oneimage and the depth data for the at least part of the real objectcorresponding to the projected digital information; and receiving userinteraction on a surface of the real object by using the subsystem torecognize touches by the user, wherein, if a certain distance of thevisible light projector to the real object is determined to be reachedor exceeded, informing a user about the need to calibrate, orautomatically starting a calibration procedure.
 2. The method accordingto claim 1, further comprising estimating a depth of the digitalinformation using the depth data.
 3. The method according to claim 1,further comprising estimating a 3D position of the digital informationusing the depth data and the at least one image.
 4. The method accordingto claim 1, wherein calculating a spatial transformation between thesubsystem of depth sensor and camera and the real object is based on a3D geometry model of the at least part of the real object and a 3Ddescription of the at least part of the real object from one or moreimages captured by the camera or depth data of the real object capturedby the depth sensor.
 5. The method according to claim 1, furthercomprising estimating a depth of the digital information using a 3Dgeometry model of the at least part of the real object and thecalculated spatial transformation between the subsystem of depth sensorand camera and the real object to gain second depth data.
 6. The methodaccording to claim 5, wherein third depth data is created using aspatial transformation between the subsystem and the visible lightprojector and intrinsic parameters of the visible light projector, andprojecting an item of digital information on the real object, which isextracted from the image of the camera.
 7. The method according to claim6, wherein the depth sensor captures the depth data as first depth data,and the method further comprises computing a difference between anycombination of the first depth data, the second depth data, and thethird depth data.
 8. The method according to claim 1, furthercomprising: projecting as the digital information at least one visualpattern onto a surface of the real object using the visible lightprojector; and capturing depth data of the projected visual patternusing the depth sensor and the camera.
 9. The method according to claim1, further comprising: calculating a spatial transformation between thevisible light projector and the subsystem; calculating or providingintrinsic parameters of the visible light projector; and computing thespatial transformation between the visible light projector and the realobject based on the spatial transformation between the visible lightprojector and the subsystem, and the spatial transformation between thesubsystem and the real object.
 10. The method according to claim 1,further comprising: transforming the depth data of the projected visualpattern from a coordinate system of the subsystem of depth sensor andcamera to an object coordinate system of the real object based on thespatial transformation between the subsystem of depth sensor and cameraand the real object; calculating or providing intrinsic parameters ofthe visible light projector; and computing the spatial transformationbetween the visible light projector and the real object based on thetransformed depth data.
 11. The method according to claim 1, wherein thesubsystem comprises a RGB-D camera system, and wherein the camera is aRGB camera.
 12. The method according to claim 1, wherein the depthsensor is capturing depth data of the at least part of the real objectwithout relying on the visible light projector.
 13. The method accordingto claim 1, wherein a distance of the visible light projector to thereal object is displayed as a visual information on the real objectusing the visible light projector.
 14. The method according to claim 1,further comprising tracking the real object using the subsystem.
 15. Themethod according to claim 14, wherein one or more visual markers areadded into the real environment to support the tracking.
 16. The methodaccording to claim 1, wherein an Iterative Closest Point algorithm isused to initialize a pose of the depth sensor.
 17. The method accordingto claim 1, wherein pose data of the visible light projector are used toset parameters of the visible light projector.
 18. The method accordingto claim 1, wherein a brightness of the visible light projector isreduced when the visible light projector gets closer to the real object.19. A non-transitory computer readable medium comprising software codesections adapted to project digital information on a real object in areal environment, the software code executable by one or more processorsto: project digital information on at least part of a real object with avisible light projector; capture at least one image of the real objectwith the projected digital information using a camera; capture, by adepth sensor registered with the camera, depth data of the at least partof the real object, wherein the depth sensor and the camera comprise asubsystem in which the depth sensor and the camera are interrelated;calculate a spatial transformation between the visible light projectorsubsystem and the real object based on the projected digital informationin the at least one image and the depth data for the at least part ofthe real object corresponding to the projected digital information; andreceive user interaction on a surface of the real object by using thesubsystem to recognize touches by the user, wherein, if a certaindistance of the visible light projector to the real object is determinedto be reached or exceeded, informing a user about the need to calibrate,or automatically starting a calibration procedure.
 20. A system forprojecting digital information on a real object in a real environment,comprising: a visible light projector adapted for projecting digitalinformation on at least part of a real object in a real environment; asubsystem comprising an interrelated camera and depth sensor, whereinthe camera is adapted for capturing at least one image of the realobject with the projected digital information, and wherein the depthsensor is registered with the camera and adapted for capturing depthdata of the at least part of the real object; and a processing unitarranged for: calculating a spatial transformation between the visiblelight projector and the real object based on the projected digitalinformation in the at least one image and the depth data for the atleast part of the real object corresponding to the projected digitalinformation, and receive user interaction on a surface of the realobject by using the subsystem to recognize touches by the user, wherein,if a certain distance of the visible light projector to the real objectis determined to be reached or exceeded, informing a user about the needto calibrate, or automatically starting a calibration procedure.
 21. Thesystem according to claim 20, wherein the camera and the depth sensorare integrated into a common housing.
 22. The system according to claim20, wherein the camera and the depth sensor are functional units of aRGB-D camera system.
 23. The system according to claim 20, wherein thevisible light projector, the camera, and the depth sensor are part of ahandheld or head-mounted device.
 24. The system according to claim 20,wherein: the camera includes a visible light camera; and the depthsensor includes an infrared light projector and an infrared lightcamera.
 25. The system according to claim 20, wherein the visible lightprojector, the camera, and the depth sensor are integrated into a commonhousing, wherein the visible light projector is separated from thecamera and the depth sensor by insulating or heat- damping material. 26.The system according to claim 20, wherein the visible light projector,the camera, and the depth sensor are integrated into a common housing,and wherein a base plate of the housing is made of a carbon fiberlaminate.
 27. The system according to claim 20, further includingcalibration means which informs the user about a need for a newcalibration of the system in case a temperature difference between atemperature of a recent calibration and a current temperature exceeds athreshold, or which automatically conducts a self-calibration of thesystem.
 28. The system according to claim 20, wherein the visible lightprojector contains at least one of the following elements: a vent, zoomoptics, and variable focus lenses.
 29. The system according to claim 20,further including an infrared light projector, wherein the infraredlight projector does not contain at least one of the following elements:a vent, zoom optics, and variable focus lenses.